Add upscaler to img2img

1 files changed, 17 insertions, 6 deletions

diff --git a/modules/processing.py b/modules/processing.py
index fc4b166c..afb8cfd1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -929,7 +929,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

     sampler = None

 

-    def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, **kwargs):

+    def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs):

         super().__init__(**kwargs)

 

         self.init_images = init_images

@@ -950,6 +950,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None

         self.image_conditioning = None

         self.scale = scale

+        self.upscaler = upscaler

 

     def get_final_size(self):

         if self.scale > 1:

@@ -966,7 +967,16 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         crop_region = None

 

         if self.scale > 1:

-            self.extra_generation_params["Img2Img Upscale"] = self.scale

+            self.extra_generation_params["Img2Img upscale"] = self.scale

+

+        # Non-latent upscalers are run before sampling

+        # Latent upscalers are run during sampling

+        init_upscaler = None

+        if self.upscaler is not None:

+            self.extra_generation_params["Img2Img upscaler"] = self.upscaler

+            if self.upscaler not in shared.latent_upscale_modes:

+                assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}"

+                init_upscaler = self.upscaler

 

         self.width, self.height = self.get_final_size()

 

@@ -992,7 +1002,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image_mask = images.resize_image(2, mask, self.width, self.height)

                 self.paste_to = (x1, y1, x2-x1, y2-y1)

             else:

-                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)

+                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler)

                 np_mask = np.array(image_mask)

                 np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)

                 self.mask_for_overlay = Image.fromarray(np_mask)

@@ -1009,7 +1019,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             image = images.flatten(img, opts.img2img_background_color)

 

             if crop_region is None and self.resize_mode != 3:

-                image = images.resize_image(self.resize_mode, image, self.width, self.height)

+                image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler)

 

             if image_mask is not None:

                 image_masked = Image.new('RGBa', (image.width, image.height))

@@ -1054,8 +1064,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 

         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))

 

-        if self.resize_mode == 3:

-            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

+        latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")

+        if latent_scale_mode is not None:

+            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])

 

         if image_mask is not None:

             init_mask = latent_mask